Wednesday, January 4, 2012


The challenges of RDBMS for massive Web-scale data processing aren’t specific to a product but pertain to the entire class of such databases. RDBMS assumes a well defined structure in data. It assumes that the data is dense and is largely uniform. RDBMS builds on a prerequisite that the properties of the data can be defined up front and that its interrelationships are well established and systematically referenced. It also assumes that indexes can be consistently defined on data sets and that such indexes can be uniformly leveraged for faster querying. Unfortunately, RDBMS starts to show signs of giving way as soon as these assumptions don’t hold true. RDBMS can certainly deal with some irregularities and lack of structure but in the context of massive sparse data sets with loosely defined structures, RDBMS appears a forced fi t. With massive data sets the typical storage mechanisms and access methods also get stretched. Denormalizing tables, dropping constraints, and relaxing transactional guarantee can help an RDBMS scale, but after these modifications an RDBMS starts resembling a NoSQL product.

Flexibility comes at a price. NoSQL alleviates the problems that RDBMS imposes and makes it easy to work with large sparse data, but in turn takes away the power of transactional integrity and flexible indexing and querying. Ironically, one of the features most missed in NoSQL is SQL, and product vendors in the space are making all sorts of attempts to bridge this gap.

Source of Information : NoSQL
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